This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment dataset using this script. In reality the multilingual-sentiment dataset is annotated of course, but we'll pretend and ignore the annotations for the sake of example. Result can be reproduce using the following commands: If you are training this model on Colab, make the following code changes to avoid Out-of-memory error message: - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Open weights
apache-2.0
135M parameters
512 tokens
transformers
A compact multilingual detector for prompt injection and jailbreak attempts (large language model guardrails). Given any user prompt, tool output, or document excerpt, it outputs the probability that the text is an attack on the LLM's instructions. Existing public injection detectors are either English-only (e.g. protectai's deberta models) or released under licenses many organizations cannot use (Meta's PromptGuard line). This model is permissively licensed, works in 17 languages, ships with its multilingual training corpus and is evaluated against a public baseline on shared test sets. Recommended threshold: 0.50 — conservative default (EN precision 0.83 on the attack test, safe-prompt…
Open weights
apache-2.0
135M parameters
512 tokens
transformers
PhoBERT Emotion v1.1 is a fine-tuned sequence classification model based on vinai/phobert-base-v2 for 7-class emotion recognition in Vietnamese social media texts: Enjoyment (Thích thú / Vui vẻ) Sadness (Buồn bã) Disgust (Chán ghét / Khinh bỉ) Anger (Tức giận) Fear (Sợ hãi) Surprise (Ngạc nhiên) Other (Khác / Trung tính) The model is trained on a curated and balanced Vietnamese social corpus (11,193 samples) with anti-overfitting techniques (Cosine Annealing scheduler, early stopping, and weight decay). In addition to standard PyTorch weights, optimized ONNX FP32 and ONNX Dynamic INT8 formats are provided for high-speed, resource-efficient CPU inference in production environments. The model…
Open weights
mit
135M parameters
258 tokens
transformers
PhoBERT Moderation v1.1 is a fine-tuned sequence classification model based on vinai/phobert-base-v2 for contextual text moderation in Vietnamese social networks, with a dedicated focus on mental health awareness and crisis intervention. Traditional keyword-based moderation often struggles to distinguish between harmless personal venting and actual malicious hate speech, or fails to detect acute emotional distress. This model implements a nuanced 4-class taxonomy: CLEAN (An toàn / Tích cực) PROFANITYVENTING (Xả bực dọc / Chửi thề vô hại) HATESPEECH (Thù ghét / Công kích xúc phạm) SELFHARMCRISIS (Khủng hoảng tâm lý / Nguy cơ tự hại) Trained on a curated Vietnamese social corpus integrating…
Open weights
mit
135M parameters
258 tokens
transformers
Model · Text classification
LiveKit
An open-weights language model for contextually-aware end-of-utterance (EOU) detection in voice AI applications. The model predicts whether a user has finished speaking based on the semantic content of their transcribed speech, providing a critical complement to voice activity detection (VAD) systems. Traditional voice agents rely on voice activity detection (VAD) to determine when a user has finished speaking. VAD works by detecting the presence or absence of speech in an audio signal and applying a silence timer. While effective for detecting pauses, VAD lacks language understanding and frequently causes false positives. For example, a user who says "I need to think about that for a…
Open weights
other
135M parameters
8,192 tokens
transformers
A fast, multilingual classifier that flags prompt injection and jailbreak attempts, both in user messages (direct) and in untrusted content an AI agent reads: emails, web pages, documents, RAG chunks, and tool/API outputs (indirect). their clean counterparts, so it looks for instructions aimed at the AI, not for scary words. - Low false-alarm rate on look-alike benign text: 89.7% on NotInject, 99.5% on OR-Bench-hard. half the size and the same decisions as fp32 on our checks), transformers.js. Labels: SAFE (0) and INJECTION (1). This is the same convention as protectai/deberta-v3-base-prompt-injection-v2, so the model is a drop-in replacement in code and tools built for that one. INJECTION…
Open weights
apache-2.0
141M parameters
8,192 tokens
transformers